| Autor: |
Moein, Mohammad, Hajiagha, Mohammadreza Molavi, Faraji, Abdolali, Tavakoli, Mohammadreza, Kismihòk, Gàbor |
| Rok vydání: |
2024 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-031-72312-4_17 |
| Popis: |
While Online Learning is growing and becoming widespread, the associated curricula often suffer from a lack of coverage and outdated content. In this regard, a key question is how to dynamically define the topics that must be covered to thoroughly learn a subject (e.g., a course). Large Language Models (LLMs) are considered candidates that can be used to address curriculum development challenges. Therefore, we developed a framework and a novel dataset, built on YouTube, to evaluate LLMs' performance when it comes to generating learning topics for specific courses. The experiment was conducted across over 100 courses and nearly 7,000 YouTube playlists in various subject areas. Our results indicate that GPT-4 can produce more accurate topics for the given courses than extracted topics from YouTube video playlists in terms of BERTScore |
| Databáze: |
arXiv |
| Externí odkaz: |
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